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- 저자 : Atena Abedi (검색결과 2 건)
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Atena Abedi,Hamidreza Ghafouri Taleghani,Mohsen Ghorbani,Hamed Salimi Kenari 한국화학공학회 2019 Korean Journal of Chemical Engineering Vol.36 No.1
Monodispersed porous silica microspheres (SM) were synthesized and further functionalized with amine moieties using triethylenetetramine (TETA) in order to obtain a novel adsorbent for Cd(II) elimination from aqueous media. The morphology, texture and structure of samples were characterized with the aid of Fourier transform infrared spectroscopy (FTIR), X-Ray diffraction (XRD), scanning and transmission electron microscopy (SEM, TEM), energy dispersive spectroscopy (EDS), and N2 adsorption-desorption. The adsorption efficiency was investigated based on the effect of operational parameters including pH of the solution, the dose of adsorbent, adsorption time, initial concentration of Cd(II) ions and temperature. The equilibrium, kinetics and thermodynamics of Cd(II) adsorption were also studied. The maximum adsorption capacity of amine functionalized silica microspheres (AMSM) for Cd(II) was 35.6mg g1. Cd(II) adsorption onto AMSM had highest consistency with Sips and Langmuir isotherms, while adsorption kinetics was best fitted with pseudo-second order model. Thermodynamics of adsorption revealed that Cd(II) adsorption on AMSM was spontaneous, feasible and exothermic with physical interactions and pore diffusion being the dominant mechanisms in the adsorption process. Results confirmed that AMSM adsorbent has the potential to be a suitable candidate for Cd(II) removal from aqueous solutions
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Mahmoudzadeh Atena,Hadavimoghaddam Fahimeh,Atashrouz Saeid,Abedi Ali,Abuswer Meftah Ali,Mohaddespour Ahmad,Hemmati-Sarapardeh Abdolhossein 한국화학공학회 2024 Korean Journal of Chemical Engineering Vol.41 No.5
Several carbon capture techniques have been developed in response to the notable rise of atmospheric carbon dioxide ( CO2 ) levels. The utilization of diethanolamine (DEA) as an absorption method is prevalent in various industries due to its high reactivity and cost-effi ciency. Hence, comprehending the equilibrium solubility of CO2 in DEA solutions is an essential step in developing and optimizing absorption procedures. In order to predict the CO2 loading capacity in the DEA solutions, four advanced deep learning and machine learning models were developed: recurrent neural networks (RNN), deep neural networks (DNN), random forest (RF), and adaBoost-support vector regression (AdaBoost-SVR). The models predict the capacity of CO2 loading as a function of temperature, CO2 partial pressure, and the concentration of DEA in the solution. Intelligent models were developed employing an extensive database which includes new experimental data points published within recent years, which were not considered in the previous studies. The RNN model was found to outperform other models based on graphical and statistical assessments, as evidenced by its lower root mean square error ( RMSE = 0.285 ) and standard deviation ( SD = 0.032 ), and higher determination coeffi cient ( R2 = 0.992 ). While the RNN model resulted in the highest accuracy in predicting CO2 absorption, the DNN, RF, and AdaBoost-SVR models also demonstrated satisfactory accuracy in predicting CO2 solubility, placed in the following ranking. A sensitivity analysis was performed on the four developed models, revealing that the CO2 partial pressure has the strongest eff ect on the CO2 loading capacity. Furthermore, a trend analysis was performed on the RNN model, demonstrating that the developed model has a high degree of accuracy in following physical trends. The binary interaction analysis was conducted with two varying parameters and one constant parameter in the RNN model through 3-D image plots, which illustrated the simultaneous eff ect of two independent parameters on CO2 loading. Finally, outlier detection was conducted by employing the Leverage method to fi nd outlier data points in the data bank, demonstrating the applicability domain of intelligent models.
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